Editorial Reviews. About the Author. Michael Morrison is a writer, developer, toy inventor, and eBook features: Highlight, take notes, and search in the book. Read "Head First Data Analysis A learner's guide to big numbers, statistics, and good decisions" by Michael Milton available from Rakuten Kobo. Sign up today. From coffee, to rubber duckies, to asking for a raise, Head First Data Analysis shows to computer programs like Excel and R, Head First Data Analysis gives .
|Language:||English, Spanish, Japanese|
|Distribution:||Free* [*Registration Required]|
How can you learn to manage and analyze all kinds of data? Turn to Head First Data Analysis, where you'll learn how to collect and organize your data, sort the. Head First Data Analysis Michael Milton Beijing • Cambridge • Farnham • Köln • Sebastopol • Tokyo - Selection from Head First Data Analysis [Book]. Head First HTML5 Programming Building Web Apps with billpercompzulbe.ga · books Head-First-Java-2nd-edition; billpercompzulbe.gais; billpercompzulbe.ga
Launching Visual Studio Latest commit 4af Sep 23, Analysis Head. Edition OReilly. Design Oreilly.
You signed in with another tab or window. Reload to refresh your session. You signed out in another tab or window.
Initial commit. Jan 17, Head First 2D Geometry. Head First Python. Head First Rails. Head First jQuery. Test your theories Its a coffee recession! The Starbuzz board meeting is in three months The Starbuzz Survey Always use the method of comparison Comparisons are key for observational data Could value perception be causing the revenue decline?
A typical customers thinking Observational studies are full of confounders How location might be confounding your results Manage confounders by breaking the data into chunks Its worse than we thought! You need an experiment to say which strategy will work best The Starbuzz CEO is in a big hurry Starbuzz drops its prices One month later Control groups give you a baseline Not getting fired Lets experiment for real!
One month later Confounders also plague experiments Avoid confounders by selecting groups carefully Randomization selects similar groups Your experiment is ready to go The results are in Starbuzz has an empirically tested sales strategy 3.
Take it to the max Youre now in the bath toy game Constraints limit the variables you control Decision variables are things you can control You have an optimization problem Find your objective with the objective function Your objective function Show product mixes with your other constraints Plot multiple constraints on the same chart Your good options are all in the feasible region Your new constraint changed the feasible region Your spreadsheet does optimization Solver crunched your optimization problem in a snap Profits fell through the floor Your model only describes what you put into it Calibrate your assumptions to your analytical objectives Watch out for negatively linked variables Your new plan is working like a charm Your assumptions are based on an ever-changing reality 4.
Data Visualization: Pictures make you smarter New Army needs to optimize their website The results are in, but the information designer is out The last information designer submitted these three infographics What data is behind the visualizations?
Show the data! Heres some unsolicited advice from the last designer Too much data is never your problem Making the data pretty isnt your problem either Data visualization is all about making the right comparisons Your visualization is already more useful than the rejected ones Use scatterplots to explore causes The best visualizations are highly multivariate Show more variables by looking at charts together The visualization is great, but the web gurus not satisfied yet Good visual designs help you think about causes The experiment designers weigh in The experiment designers have some hypotheses of their own The client is pleased with your work Orders are coming in from everywhere!
Hypothesis Testing: Say it aint so Gimme some skin When do we start making new phone skins?
PodPhone doesnt want you to predict their next move Heres everything we know ElectroSkinnys analysis does fit the data ElectroSkinny obtained this confidential strategy memo Variables can be negatively or positively linked Causes in the real world are networked, not linear Hypothesize PodPhones options You have what you need to run a hypothesis test Falsification is the heart of hypothesis testing Diagnosticity helps you find the hypothesis with the least disconfirmation You cant rule out all the hypotheses, but you can say which is strongest You just got a picture message Its a launch!
Bayesian Statistics: Get past first base The doctor has disturbing news Lets take the accuracy analysis one claim at a time How common is lizard flu really? Youve been counting false positives The opposite of a false positive is a true negative All these terms describe conditional probabilities You need to count 1 percent of people have lizard flu Watch out for the base rate fallacy Your chances of having lizard flu are still pretty low Do complex probabilistic thinking with simple whole numbers Bayes rule manages your base rates when you get new data You can use Bayes rule over and over Your second test result is negative The new test has different accuracy statistics New information can change your base rate What a relief!
Subjective Probabilities: Numerical belief Backwater Investments needs your help Their analysts are at each others throats Subjective probabilities describe expert beliefs Subjective probabilities might show no real disagreement after all The analysts responded with their subjective probabilities The CEO doesnt see what youre up to The CEO loves your work The standard deviation measures how far points are from the average You were totally blindsided by this news Bayes rule is great for revising subjective probabilities The CEO knows exactly what to do with this new information Russian stock owners rejoice!
Analyze like a human LitterGitters submitted their report to the city council The LitterGitters have really cleaned up this town The LitterGitters have been measuring their campaigns effectiveness The mandate is to reduce the tonnage of litter Tonnage is unfeasible to measure Give people a hard question, and theyll answer an easier one instead Littering in Dataville is a complex system You cant build and implement a unified litter-measuring model Heuristics are a middle ground between going with your gut and optimization Use a fast and frugal tree Is there a simpler way to assess LitterGitters success?
Stereotypes are heuristics Your analysis is ready to present Looks like your analysis impressed the city council members 9.
The shape of numbers Your annual review is coming up Going for more cash could play out in a bunch of different ways Heres some data on raises Histograms show frequencies of groups of numbers Gaps between bars in a histogram mean gaps among the data points Install and run R Load data into R R creates beautiful histograms Make histograms from subsets of your data Negotiation pays What will negotiation mean for you?
Prediction What are you going to do with all this money? An analysis that tells people what to ask for could be huge Behold Inside the algorithm will be a method to predict raises Scatterplots compare two variables A line could tell your clients where to aim Predict values in each strip with the graph of averages The regression line predicts what raises people will receive The line is useful if your data shows a linear correlation You need an equation to make your predictions precise a represents the y-axis intercept b represents the slope Tell R to create a regression object The regression equation goes hand in hand with your scatterplot The regression equation is the Raise Reckoner algorithm Your raise predictor didnt work out as planned Err Well Your clients are pretty ticked off What did your raise prediction algorithm do?
Chance errors are deviations from what your model predicts Error is good for you and your client Specify error quantitatively Quantify your residual distribution with Root Mean Squared error Your model in R already knows the R. Relational Databases: Can you relate?
The Dataville Dispatch wants to analyze sales Heres the data they keep to track their operations You need to know how the data tables relate to each other A database is a collection of data with well-specified relations to each other Trace a path through the relations to make the comparison you need Create a spreadsheet that goes across that path Your summary ties article count and sales together Looks like your scatterplot is going over really well Copying and pasting all that data was a pain Relational databases manage relations for you Dataville Dispatch built an RDBMS with your relationship diagram Dataville Dispatch extracted your data using the SQL language Comparison possibilities are endless if your data is in a RDBMS Youre on the cover Cleaning Data: Sort your data to show duplicate values together The data is probably from a relational database Remove duplicate names You created nice, clean, unique records Head First Head Hunters is recruiting like gangbusters!
Leaving town Its been great having you here in Dataville! The Top Ten Things we didnt cover 1: Everything else in statistics 2: Excel skills 3: